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%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/


%% Created for Nick at 2011-01-20 16:55:26 +0100 


%% Saved with string encoding Unicode (UTF-8) 



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	Date-Added = {2011-01-17 15:26:09 +0100},
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author = 	 {Grisetti, G. and Grzonka, S. and  Stachniss, C. and Pfaff, P. and Burgard, W.},
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  booktitle =    {IROS},
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  pages = {3472--3478},
  address  = 	 {San Diego, CA (USA)}
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url = "http://www.sciencedirect.com/science/article/B6WCX-4RC2S4T-2/2/c2c03b6165996e30312e5b7c7b681155",
author = "Herbert Bay and Andreas Ess and Tinne Tuytelaars and Luc Van Gool",
keywords = "Interest points",
keywords = "Local features",
keywords = "Feature description",
keywords = "Camera calibration",
keywords = "Object recognition"
}


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	Date-Added = {2011-01-13 17:49:50 +0100},
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@article{steder2008visual,
	Annote = {If a stereo setup is available, our approach is able to learn visual elevation maps of the ground. If, however, only one camera is carried by the vehicle, our system can be applied by making a flat ground assumption providing a visual map without elevation information.


Abstract
We assume that the vehicles are equipped with one or two low-cost downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation.
Our technique uses visual features and estimates the correspondences between features using a variant of the progressive sample consensus (PROSAC) algorithm.
Furthermore, we address the problem of efficiently identifying loop closures.


INTRODUCTION
several approaches for building 3-D maps have been pro- posed [8], [15], [21]. However, most of these methods rely on bulky sensors that have a high range and accuracy.
Several researchers focused on utilizing vision sensors instead of laser range finders.

In this paper, we present a system that allows aerial vehicles to ac- quire visual maps of large environments using an attitude sensor and low-quality cameras pointing downward.

Furthermore, it can operate in two different configurations: with a stereo as well as with a monocular camera. If a stereo setup is available, our approach is able to learn visual elevation maps of the ground. If, however, only one camera is carried by the vehicle, our system can be applied by making a flat ground assumption providing a visual map without elevation information. To simplify the problem, we used an attitude (roll and pitch) sensor.


RELATED WORK
Celemente et al Integrated the gaussian particle technique in a hierarchical SLAM framework, which has been reported to successfully build large-scale maps with comparably poor sensors.
While the feature extraction can be performed at low frequency, the movement of the robot is constantly estimated by tracking the interest points at high frequency.

Andreasson et al. [1] presented a technique that is based on a local similarity measure for images. They store reference images at different locations and use these references as a map. In this way, their approach is reported to scale well with the size of the envi- ronment.

Our approach uses a similar SLAM formulation but computes the synthetic measurements between poses based on an efficient pairwise frame alignment technique.

We furthermore apply a highly efficient error minimization approach [9], which is an extension of the research of Olson et al. [16].


GRAPH-BASED SLAM
Throughout this paper, we consider the SLAM problem in its graph-based formulation.
Accordingly, the poses of the robot are described by the nodes of a graph. Edges between these nodes represent spatial constraints between them. They are typically constructed from obser- vations or from odometry. Under this formulation, a solution to the SLAM problem is a configuration of the nodes that minimizes the error introduced by the constraints.

The spatial constraints between two poses are computed from the camera images and the attitude measurements.

We use speeded-up robust features (SURF) [2] that are invariant with respect to rotation and scale.

By matching features between different images, one can estimate the relative motion of the camera, and thus, construct the graph that serves as input to the optimizer.

In addition to that, the attitude sensor provides the roll and pitch angle of the camera. In our experiments, we found that the roll and the pitch measurements are comparably accurate even for low-cost sensors and can be directly integrated into the estimate.


SPATIAL RELATION BETWEEN CAMERA POSES
Odometry describes the relative motion between subsequent poses. To obtain an odometry estimate, we match the features in the current image to the ones stored in the previous n nodes.
Since the number of features found during place revisiting can be quite high, we introduce further approximations in the search procedure.
These features are the ones that were better matched when computing visual odometry (which have the lowest descriptor distance). Second, we apply a k-D tree to efficiently query for similar features, and we use the best-bins-first technique proposed by Lowe [14].

Since we only need two correspondences to compute the camera transformation, the chances that the algorithm gets stuck due to wrong correspondences are very small.


EXPERIMENTS
We used only real world data that we partially recorded with a sensor platform carried in the hand of a person as well as with a real blimp and a helicopter.},
	Author = {Steder, B. and Grisetti, G. and Stachniss, C. and Burgard, W.},
	Date-Added = {2011-01-13 17:42:12 +0100},
	Date-Modified = {2011-01-19 12:25:59 +0100},
	Issn = {1552-3098},
	Journal = {Robotics, IEEE Transactions on},
	Number = {5},
	Pages = {1088--1093},
	Publisher = {IEEE},
	Read = {1},
	Title = {{Visual SLAM for flying vehicles}},
	Volume = {24},
	Year = {2008},
        url={http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4636756},
}

@book{steffen2009visual,
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	Date-Added = {2011-01-13 17:40:28 +0100},
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	Read = {1},
	Title = {{Visual SLAM from image sequences acquired by unmanned aerial vehicles}},
	Year = {2009},
}

@article{nemra2009robust,
	Author = {Nemra, A. and Aouf, N.},
	Date-Added = {2011-01-13 17:38:36 +0100},
	Date-Modified = {2011-01-13 17:38:36 +0100},
	Issn = {0921-0296},
	Journal = {Journal of Intelligent and Robotic Systems},
	Number = {4},
	Pages = {345--376},
	Publisher = {Springer},
	Title = {{Robust Airborne 3D Visual Simultaneous Localization and Mapping with Observability and Consistency Analysis}},
	Volume = {55},
	Year = {2009},
}

@article{bryson2008observability,
	Author = {Bryson, M. and Sukkarieh, S.},
	Date-Added = {2011-01-13 17:35:18 +0100},
	Date-Modified = {2011-01-13 17:35:18 +0100},
	Issn = {0018-9251},
	Journal = {Aerospace and Electronic Systems, IEEE Transactions on},
	Number = {1},
	Pages = {261--280},
	Publisher = {IEEE},
	Title = {{Observability analysis and active control for airborne SLAM}},
	Volume = {44},
	Year = {2008},
}

@article{kim2007real,
	Author = {Kim, J. and Sukkarieh, S.},
	Date-Added = {2011-01-13 17:34:25 +0100},
	Date-Modified = {2011-01-13 17:34:25 +0100},
	Issn = {0921-8890},
	Journal = {Robotics and Autonomous Systems},
	Number = {1},
	Pages = {62--71},
	Publisher = {Elsevier},
	Title = {{Real-time implementation of airborne inertial-SLAM}},
	Volume = {55},
	Year = {2007},
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@conference{biber20053d,
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	Date-Added = {2011-01-13 17:32:54 +0100},
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	Isbn = {0780384636},
	Organization = {IEEE},
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	Volume = {4},
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@article{klein2008improving,
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	Date-Added = {2011-01-13 17:31:43 +0100},
	Date-Modified = {2011-01-13 17:31:43 +0100},
	Journal = {Computer Vision--ECCV 2008},
	Pages = {802--815},
	Publisher = {Springer},
	Title = {{Improving the agility of keyframe-based SLAM}},
	Year = {2008},
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@article{milford2008mapping,
	Author = {Milford, M.J. and Wyeth, G.F.},
	Date-Added = {2011-01-13 17:28:06 +0100},
	Date-Modified = {2011-01-13 17:28:06 +0100},
	Issn = {1552-3098},
	Journal = {Robotics, IEEE Transactions on},
	Number = {5},
	Pages = {1038--1053},
	Publisher = {IEEE},
	Title = {{Mapping a suburb with a single camera using a biologically inspired SLAM system}},
	Volume = {24},
	Year = {2008},
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@article{davison2007monoslam,
	Author = {Davison, A.J. and Reid, I.D. and Molton, N.D. and Stasse, O.},
	Date-Added = {2011-01-13 17:26:25 +0100},
	Date-Modified = {2011-01-13 17:26:25 +0100},
	Issn = {0162-8828},
	Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	Pages = {1052--1067},
	Publisher = {Published by the IEEE Computer Society},
	Title = {{MonoSLAM: Real-time single camera SLAM}},
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@article{durrant2006simultaneous,
	Annote = {SLAM is a process by which a mobile robot can build a map of an environ- ment and at the same time use this map to deduce its location. In SLAM, both the trajectory of the platform and the location of all landmarks are estimated online without the need for any a priori knowledge of location.
At a theoretical and conceptual level, SLAM can now be considered a solved problem. However, substantial issues remain in practically realizing more general SLAM solutions and notably in building and using perceptually rich maps as part of a SLAM algorithm.
The solution section describes the two key computational solutions to the SLAM problem through the use of the extended Kalman filter (EKF-SLAM) and through the use of Rao-Blackwellized particle filters (FastSLAM).

A consistent full solution to the combined localization and mapping problem would require a joint state composed of the vehicle pose and every landmark position, to be updated following each landmark observation. The conceptual breakthrough came with the realization that the combined mapping and localization problem, once formulated as a single estimation problem, was actually convergent.


Probabilistic SLAM
In probabilistic form, the simultaneous localization and map building (SLAM) problem requires that the probability distribution be computed for all times k.  In general, a recursive solution to the SLAM problem is desirable. 
- The observation model describes the probability of making an observation zk when the vehicle location and landmark locations are known.
- The motion model for the vehicle can be described in terms of a probability distribution on state transitions.
The state transition is assumed to be a Markov process in which the next state xk depends only on the immediately preceding state xk−1 and the applied control uk and is inde- pendent of both the observations and the map.
The SLAM algorithm is now implemented in a standard two-step recursive (sequential) prediction (time-update) correction (measurement-update) form.

However, the SLAM problem has more structure than is immediately obvious from these equations:
- The errors in landmark location estimates are highly correlated.
- The joint probability density for the pair of landmarks P(mi,mj) is highly peaked even when the marginal densities P(mi) may be quite dispersed.
Practically, this means that knowledge of the relative location of landmarks always improves and never diverges, regardless of robot motion.
As the map is built, the location accuracy of the robot measured relative to the map is bounded only by the quality of the map and relative measurement sensor.


Solutions to the SLAM Problem
Solutions to the probabilistic SLAM problem involve find- ing an appropriate representation for both the observation model (2) and motion model (3) that allows efficient and consistent computation of the prior and posterior distributions.
Two popular solutions:
1. extended Kalman filter (EKF) (representa- tion is in the form of a state-space model with additive Gaussian noise)
2. Rao-Blackwellized particle filter/FastSLAM (describe the vehicle motion model in (3) as a set of samples of a more general non- Gaussian probability distribution).

FastSLAM:
The probability distribution is on the trajectory X0:k rather than the single pose xk because, when conditioned on the trajectory, the map landmarks become independent (see Figure 4). This is a key property of FastSLAM and the reason for its speed; the map is represented as a set of independent Gaussians, with linear complexity, rather than a joint map covariance with quadratic complexity.

},
	Author = {Durrant-Whyte, H. and Bailey, T.},
	Date-Added = {2011-01-13 17:22:45 +0100},
	Date-Modified = {2011-01-15 17:18:16 +0100},
	Issn = {1070-9932},
	Journal = {IEEE Robotics \& Automation Magazine},
	Number = {2},
	Pages = {99--110},
	Read = {1},
	Title = {{Simultaneous localization and mapping: part I}},
	Volume = {13},
	Year = {2006},
}

@article{bailey2006simultaneous,
	Annote = {SLAM, in its naive form, scales quadratically with the number of landmarks in a map.

Computational Complexity
The state-based formulation of the SLAM problem involves the estimation of a joint state composed of a robot pose and the locations of observed stationary landmarks.
Optimal algorithms aim to reduce required computation while still resulting in estimates and covariances that are equal to the full-form SLAM algorithm (as presented in Part I of this tutorial). Conservative algorithms result in estimates that have larger uncertainty or covariance than the optimal result. 
The direct approach to reducing computational complex-
ity involves exploiting the structure of the SLAM problem in re-formulating the essential time- and observation-update equations to limit required computation.
The time-update computation can be limited using state-augmentation methods. The observation-update computation can be limited using a partitioned form of the update equations.
Submapping methods exploit the idea that a map can be broken up into regions with local coordinate systems and arranged in a hierarchical manner. Updates can occur in a local frame with periodic interframe updates.

Partitioned Updates
There are two basic types of partitioned update:
1. operates in a local region of the global map and maintains globally referenced coordinates.
2. generates a short-term submap with its own local coordinate frame.
The latter approach as it is simpler and, by performing high-frequency operations in a local coordinate frame, it avoids very large global covari- ances and, therefore, is more numerically stable and less affected by linearization errors.

Sparsification
The advantage of the information form for SLAM is that, for large-scale maps, many of the off-diagonal components of the normalized information matrix are very close to zero.
With the information matrix now sparse, very efficient update procedures for information estimates can be obtained with relatively little loss in optimality of the maps produced.
By judicious selection of anchoring poses to decouple different regions of the map, a great proportion of poses can be marginalized away without inducing excessive density.

Global Submaps
Global submap methods estimate the global locations of submap coordinate frames relative to a common base frame and  conservative estimate of the global map.

Relative Submaps
Relative submap methods differ from global submaps in that there is no common coordinate frame. The loca- tion of any given submap is recorded only by its neighboring submaps, and these are connected in a graphical network. Global estimates can be obtained by vector summation along a path in the network. 

Data Association
Data association has always been a critical issue for practical SLAM implementations. Before fusing data into the map, new measurements are associated with existing map land- marks, and, after fusion, these associations cannot be revised.
The problem is that a single incorrect data association can induce divergence into the map estimate. 

Batch Validation
An important advance was the concept of batch gating, where multiple associations are considered simultaneously. Mutual association compatibility exploits the geometric relationship between landmarks.

Appearance Signatures
Appearance signatures are useful to predict a possible association, such as closing a loop, or for assisting conventional gating by providing additional discrimination information.
A similarity met- ric over a sequence of images, rather than a single image, is computed, and an eigenvalue technique is employed to remove common-mode similarity. This approach consider- ably reduces the occurrence of false positives by considering only matches that are interesting or uncommon.

Environment Representation
Early work in SLAM assumed that the world could reasonably be modeled as a set of simple discrete landmarks described by geometric primitives. For more complex environments, this does not hold.

Partial Observability and Delayed Mapping
Two common examples are sonar and vision.
SLAM with range-only sensors [32], [33] and bearing- only sensors [3], [11] shows that a single measurement is insufficient to constrain a landmark location. Rather, it must be observed from multiple vantage points.
Generalized distributions, such as mixture models, permit immediate, nondelayed landmark tracking.

3-D SLAM
Involves significant added complexity due to the more general vehicle motion model and, most importantly, greatly increased sensing and feature modeling complexity.
The first is simply 2-D SLAM with additional map building capabilities in the third dimension, for example, hor- izontal laser-based SLAM with a second orthogonal laser mapping vertical slices [35], [46]. This approach is appropriate when the vehicle motion is confined to a plane.
The third form involves an entirely differ- ent SLAM formulation, where the joint state is composed of a history of past vehicle poses [16], [39]. At each pose, the vehi- cle obtains a 3-D scan of the environment, and the pose esti- mates are aligned by correlating the scans.

!!!Trajectory-Oriented SLAM
An alternative formulation of the SLAM problem that has gained recent popularity is to estimate the vehicle trajec- tory instead.This formulation is particularly suited to environments where discrete identifiable landmarks are not easily discerned and direct alignment of sensed data is simpler or more reliable.
this formulation of the SLAM problem has no explicit map; rather, each pose estimate has an associated scan of sensed data, and these are aligned to form a global map. [39]
Several recent FastSLAM hybrids use pose-aligned scans or grids in place of a landmark map [14], [20], [24].

Embedded Auxiliary Information
Trajectory-based SLAM lends itself to representing spatially located information. Besides scan data for mapping, it is pos- sible to associate auxiliary information with each pose, soil salinity, humidity, temperature, or terrain characteristics, for example. The associated information may be used to assist mapping, to aid data association, or for purposes unrelated to the mapping task. As the robot moves through the environment, auxiliary data is stored in a suitable data structure, such as an occupancy grid, and the region repre- sented by each grid cell is determined by a set of local land- marks in the SLAM map.


SLAM: Where to Next?
The standard state-space approach to SLAM is now well understood, and the main issues in representation, computa- tion, and association appear to be resolved. The information form of the SLAM problem has significant unexplored potential in large-scale mapping, problems involving many vehicles and potentially in mixed environments with sensor networks and dynamic landmarks. 

Appearance- and pose-based SLAM methods offer a radically new paradigm for mapping and location estimation without the need for strong geometric landmark descriptions.

The key challenges for SLAM are in larger and more per- suasive implementations and demonstrations.

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@article{engels2006bundle,
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